The transformation is found through iterative algorithms [1]}, [2]}, [3]}, [4]}, [5]}, or predicted with learning based techniques [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}. In both cases, the optimal transformation is found by minimization of a similarity measure \(D\)  and a \(\lambda \) -weighted regularizer \(R\) , expressed via the loss function
\( L(I, J, \Phi ) = D(I\circ \Phi , J) + \lambda R(\Phi ) \hspace{5.0pt}.\) 
